4.3. Style Advanced — Python
4.3.1. Label rotation
Code 4.7. Label rotation
import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [1, 4, 9, 6] labels = ['Frogs', 'Hogs', 'Bogs', 'Slogs'] plt.plot(x, y, 'ro') # You can specify a rotation for the tick labels in degrees or with keywords. plt.xticks(rotation=45) # Pad margins so that markers don't get clipped by the axes plt.margins(0.2) # Tweak spacing to prevent clipping of tick-labels plt.subplots_adjust(bottom=0.15) plt.show()
Figure 4.22. Label rotation
4.3.2. Grid
Code 4.8. Grid Simple
import matplotlib.pyplot as plt x = [1, 2, 3, 4, 5] y = [1, 2, 3, 4, 5] plt.plot(x, y) plt.grid(True) plt.show()
Figure 4.23. Grid Simple
Code 4.9. Grid Extra
import numpy as np import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # Major ticks every 20, minor ticks every 5 major_ticks = np.arange(0, 101, 20) minor_ticks = np.arange(0, 101, 5) ax.set_xticks(major_ticks) ax.set_xticks(minor_ticks, minor=True) ax.set_yticks(major_ticks) ax.set_yticks(minor_ticks, minor=True) # And a corresponding grid ax.grid(which='both') # Or if you want different settings for the grids: ax.grid(which='minor', alpha=0.2) ax.grid(which='major', alpha=0.5) plt.show()
Figure 4.24. Grid Extra
4.3.3. Trend line
Code 4.10. Trend line
import matplotlib.pylab as pylab import numpy as np x = [1, 3, 5, 7, 9] y = [2, 3, 4, 3, 4] # plot the data itself pylab.plot(x, y, label="data") # calc the trendline (it is simply a linear fitting) z = np.polyfit(x, y, 1) p = np.poly1d(z) pylab.plot(x, p(x), color="red", linestyle='--') # the line equation: a = z[0] b = z[1] print(f"y = {a:.6}x + ({b:.6})") # parabolic fit will be: # z = numpy.polyfit(x, y, 2)
Figure 4.25. Trend line
4.3.4. Error bars
Code 4.11. Error bars
import matplotlib.pyplot as plt x = [1, 2, 3, 4] y = [1, 4, 9, 16] e = [0.5, 1., 1.5, 2.] plt.errorbar(x, y, yerr=e, fmt='o') plt.show()
Figure 4.26. Error bars
4.3.5. Colorbar
Code 4.12. Colorbar
from matplotlib import pyplot as plt from sklearn.datasets import load_iris iris = load_iris() # The indices of the features that we are plotting x_index = 0 y_index = 1 # this formatter will label the `colorbar` with the correct target names formatter = plt.FuncFormatter(lambda i, *args: iris.target_names[int(i)]) plt.figure(figsize=(5, 4)) plt.scatter(iris.data[:, x_index], iris.data[:, y_index], c=iris.target) plt.colorbar(ticks=[0, 1, 2], format=formatter) plt.xlabel(iris.feature_names[x_index]) plt.ylabel(iris.feature_names[y_index]) plt.tight_layout() plt.show()
Figure 4.27. Colorbar
4.3.6. Changing colors
ax.spines['bottom'].set_color('#dddddd') ax.spines['top'].set_color('#dddddd') ax.spines['right'].set_color('red') ax.spines['left'].set_color('red')
ax.tick_params(axis='x', colors='red') ax.tick_params(axis='y', colors='red')
ax.yaxis.label.set_color('red') ax.xaxis.label.set_color('red')
ax.title.set_color('red')
4.3.7. Using mathematical expressions in text
plt.title(r'$\sigma_i=15$')
4.3.8. Annotations
4.3.9. Single Annotation
import matplotlib.pyplot as plt import numpy as np ax = plt.subplot(111) t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) line, = plt.plot(t, s, lw=2) plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops={'facecolor': 'black', 'shrink': 0.05}, ) plt.ylim(-2,2) plt.show() # doctest: +SKIP
4.3.10. Multiple Annotations
Code 4.13. Multiple Annotations
import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 10, 0.005) y = np.exp(-x / 2.) * np.sin(2 * np.pi * x) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(x, y) ax.set_xlim(0, 10) ax.set_ylim(-1, 1) data_x = 5 data_y = 0 display_x, display_y = ax.transData.transform_point((data_x, data_y)) bbox = dict(boxstyle="round", fc="0.8") arrowprops = dict( arrowstyle="->", connectionstyle="angle,angleA=0,angleB=90,rad=10") offset = 72 ax.annotate('data = (%.1f, %.1f)' % (data_x, data_y), (data_x, data_y), xytext=(-2 * offset, offset), textcoords='offset points', bbox=bbox, arrowprops=arrowprops) display = ax.annotate('display = (%.1f, %.1f)' % (display_x, display_y), (display_x, display_y), xytext=(0.5 * offset, -offset), xycoords='figure pixels', textcoords='offset points', bbox=bbox, arrowprops=arrowprops) plt.show()
